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Related Concept Videos

Flame Photometry: Overview01:02

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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
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The low reactivity in alkanes can be attributed to the non-polar nature of C–C and C–H σ bonds. Alkanes, therefore, were  initially termed as “paraffins,” derived from the Latin words: parum, meaning “too little,” and affinis, meaning “affinity.”
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The internal combustion engine is a heat engine that uses the byproducts of combustion as the working fluid instead of using a heat transfer medium to transfer heat. The combustion is done in a way that produces high-pressure combustion products that can be expanded through a turbine or piston to create work. Internal combustion engines can again be categorized into three kinds: (1) spark ignition gasoline engines, most commonly used in automobiles, (2) compression ignition diesel engines that...
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions.

Ahmed Ramdan Almaqtouf Algwil1, Wagdi M S Khalifa2

  • 1Cyprus Health and Social Sciences University, Mersin 10, Turkey. 220928004@kstu.edu.tr.

Scientific Reports
|April 8, 2025
PubMed
Summary

A new hybrid model, Gaussian mutation and shrink mechanism-based moth flame optimization with extreme learning machine (GMSMFO-ELM), accurately predicts CO2 emissions. This advanced approach supports global sustainability goals with high predictive accuracy.

Keywords:
Carbon dioxide (CO2) emissions predictionExtreme learning machine (ELM)Machine learning (ML)Moth flame optimization (MFO)

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Area of Science:

  • Environmental Science
  • Computer Science
  • Optimization Algorithms

Background:

  • Accurate prediction of carbon dioxide (CO2) emissions is crucial for effective environmental policy and sustainable development.
  • Existing prediction models often struggle with accuracy and adaptability to complex emission patterns.
  • Developing robust hybrid models can significantly enhance predictive capabilities for CO2 emissions.

Purpose of the Study:

  • To introduce and evaluate a novel hybrid model, GMSMFO-ELM, for precise CO2 emissions prediction.
  • To demonstrate the effectiveness of the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm in optimizing machine learning parameters.
  • To provide a reliable framework for informed decision-making in global sustainability initiatives.

Main Methods:

  • Integration of the GMSMFO algorithm with the extreme learning machine (ELM) for a hybrid predictive model.
  • Utilizing Gaussian mutation (GM) for enhanced population diversity and the shrink mechanism (SM) for improved exploration-exploitation balance within GMSMFO.
  • Validation of GMSMFO on the CEC2020 benchmark suite and application to fine-tune ELM for CO2 emissions prediction.

Main Results:

  • The GMSMFO algorithm showed superior performance over other optimization techniques on benchmark datasets.
  • The GMSMFO-ELM model achieved a high coefficient of determination (R2) of 96.5% for CO2 emissions prediction.
  • The model outperformed existing hybrid approaches in key metrics like RMSE, NRMSE, MAE, and MSE.
  • Economic growth, foreign direct investment, and renewable energy were identified as significant predictors of CO2 emissions.

Conclusions:

  • The GMSMFO-ELM model demonstrates robust and adaptable performance for accurate CO2 emissions prediction.
  • This hybrid approach offers a reliable tool for advancing global sustainability objectives.
  • The study underscores the potential of advanced optimization algorithms in enhancing environmental modeling and policy support.